Multi-label Feature Transform for Image Classifications

Image and video annotations are challenging but important tasks to understand digital multimedia contents in computer vision, which by nature is a multi-label multi-class classification problem because every image is usually associated with more than one semantic keyword. As a result, label assignments are no longer confined to class membership indications as in traditional single-label multi-class classification, which also convey important characteristic information to assess object similarity from knowledge perspective. Therefore, besides implicitly making use of label assignments to formulate label correlations as in many existing multi-label classification algorithms, we propose a novel Multi-Label Feature Transform (MLFT) approach to also explicitly use them as part of data features. Through two transformations on attributes and label assignments respectively, MLFT approach uses kernel to implicitly construct a label-augmented feature vector to integrate attributes and labels of a data set in a balanced manner, such that the data discriminability is enhanced because of taking advantage of the information from both data and label perspectives. Promising experimental results on four standard multi-label data sets from image annotation and other applications demonstrate the effectiveness of our approach.